{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Swin.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tp2rgTJj_1bU",
        "outputId": "5f377b38-4c1d-49a2-b93e-7c99091c4310"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu May 13 14:16:08 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   58C    P8    12W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0KwnYfBc6vb",
        "outputId": "75ae2ac5-cb81-4439-d49a-91fce3a50a63"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ap8gcmG80Xui",
        "outputId": "c727483d-64c1-4abd-f3de-cfde0eacf660"
      },
      "source": [
        "!git clone https://github.com/SwinTransformer/Swin-Transformer-Object-Detection.git"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Swin-Transformer-Object-Detection'...\n",
            "remote: Enumerating objects: 16890, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (12/12), done.\u001b[K\n",
            "remote: Total 16890 (delta 9), reused 14 (delta 9), pack-reused 16869\u001b[K\n",
            "Receiving objects: 100% (16890/16890), 20.28 MiB | 34.16 MiB/s, done.\n",
            "Resolving deltas: 100% (11706/11706), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_E1HU8yppFh",
        "outputId": "ce62ec63-e38c-4b56-d4bb-e3deebd75f73"
      },
      "source": [
        "%cd /content/Swin-Transformer-Object-Detection\n",
        "!wget https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.pth"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Swin-Transformer-Object-Detection\n",
            "--2021-05-13 14:16:18--  https://github.com/SwinTransformer/storage/releases/download/v1.0.3/mask_rcnn_swin_tiny_patch4_window7_1x.pth\n",
            "Resolving github.com (github.com)... 140.82.114.4\n",
            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-releases.githubusercontent.com/357198522/34197980-b273-11eb-9c78-7134da7d6b14?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210513%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210513T141618Z&X-Amz-Expires=300&X-Amz-Signature=187da90b7228324a5590c5f00705a2255708f2ae05c52b703a1b46ff667a610a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=357198522&response-content-disposition=attachment%3B%20filename%3Dmask_rcnn_swin_tiny_patch4_window7_1x.pth&response-content-type=application%2Foctet-stream [following]\n",
            "--2021-05-13 14:16:18--  https://github-releases.githubusercontent.com/357198522/34197980-b273-11eb-9c78-7134da7d6b14?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20210513%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210513T141618Z&X-Amz-Expires=300&X-Amz-Signature=187da90b7228324a5590c5f00705a2255708f2ae05c52b703a1b46ff667a610a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=357198522&response-content-disposition=attachment%3B%20filename%3Dmask_rcnn_swin_tiny_patch4_window7_1x.pth&response-content-type=application%2Foctet-stream\n",
            "Resolving github-releases.githubusercontent.com (github-releases.githubusercontent.com)... 185.199.108.154, 185.199.109.154, 185.199.110.154, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 191487694 (183M) [application/octet-stream]\n",
            "Saving to: ‘mask_rcnn_swin_tiny_patch4_window7_1x.pth’\n",
            "\n",
            "mask_rcnn_swin_tiny 100%[===================>] 182.62M  58.3MB/s    in 3.1s    \n",
            "\n",
            "2021-05-13 14:16:22 (58.3 MB/s) - ‘mask_rcnn_swin_tiny_patch4_window7_1x.pth’ saved [191487694/191487694]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8zTiVew6sT3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "464e4c6f-3590-41d8-8226-de704bcd4644"
      },
      "source": [
        "!pip install mmcv-full==1.3.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/index.html\n",
            "Collecting mmcv-full==1.3.2\n",
            "\u001b[?25l  Downloading https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.0/mmcv_full-1.3.2-cp37-cp37m-manylinux1_x86_64.whl (28.3MB)\n",
            "\u001b[K     |████████████████████████████████| 28.3MB 112kB/s \n",
            "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.3.2) (7.1.2)\n",
            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.3.2) (4.1.2.30)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.3.2) (1.19.5)\n",
            "Collecting addict\n",
            "  Downloading https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl\n",
            "Collecting yapf\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5f/0d/8814e79eb865eab42d95023b58b650d01dec6f8ea87fc9260978b1bf2167/yapf-0.31.0-py2.py3-none-any.whl (185kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 27.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full==1.3.2) (3.13)\n",
            "Installing collected packages: addict, yapf, mmcv-full\n",
            "Successfully installed addict-2.4.0 mmcv-full-1.3.2 yapf-0.31.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86ro7kT2JJXm",
        "outputId": "e36a3a92-0c8e-4ad6-fcb4-c0ae394cd5ef"
      },
      "source": [
        "%cd /content/Swin-Transformer-Object-Detection\n",
        "!pip install -r requirements/build.txt\n",
        "!python setup.py develop"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Swin-Transformer-Object-Detection\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from -r requirements/build.txt (line 2)) (0.29.23)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from -r requirements/build.txt (line 3)) (1.19.5)\n",
            "running develop\n",
            "running egg_info\n",
            "creating mmdet.egg-info\n",
            "writing mmdet.egg-info/PKG-INFO\n",
            "writing dependency_links to mmdet.egg-info/dependency_links.txt\n",
            "writing requirements to mmdet.egg-info/requires.txt\n",
            "writing top-level names to mmdet.egg-info/top_level.txt\n",
            "writing manifest file 'mmdet.egg-info/SOURCES.txt'\n",
            "adding license file 'LICENSE' (matched pattern 'LICEN[CS]E*')\n",
            "writing manifest file 'mmdet.egg-info/SOURCES.txt'\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/cpp_extension.py:369: UserWarning: Attempted to use ninja as the BuildExtension backend but we could not find ninja.. Falling back to using the slow distutils backend.\n",
            "  warnings.warn(msg.format('we could not find ninja.'))\n",
            "running build_ext\n",
            "Creating /usr/local/lib/python3.7/dist-packages/mmdet.egg-link (link to .)\n",
            "Adding mmdet 2.11.0 to easy-install.pth file\n",
            "\n",
            "Installed /content/Swin-Transformer-Object-Detection\n",
            "Processing dependencies for mmdet==2.11.0\n",
            "Searching for timm\n",
            "Reading https://pypi.org/simple/timm/\n",
            "Downloading https://files.pythonhosted.org/packages/9e/89/d94f59780b5dd973154bf506d8ce598f6bfe7cc44dd445d644d6d3be8c39/timm-0.4.5-py3-none-any.whl#sha256=8699f644a60527005db07f31ceffef5349c4d144749e1243733c64220a7d6f92\n",
            "Best match: timm 0.4.5\n",
            "Processing timm-0.4.5-py3-none-any.whl\n",
            "Installing timm-0.4.5-py3-none-any.whl to /usr/local/lib/python3.7/dist-packages\n",
            "Adding timm 0.4.5 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.7/dist-packages/timm-0.4.5-py3.7.egg\n",
            "Searching for terminaltables\n",
            "Reading https://pypi.org/simple/terminaltables/\n",
            "Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz#sha256=f3eb0eb92e3833972ac36796293ca0906e998dc3be91fbe1f8615b331b853b81\n",
            "Best match: terminaltables 3.1.0\n",
            "Processing terminaltables-3.1.0.tar.gz\n",
            "Writing /tmp/easy_install-wodfcaun/terminaltables-3.1.0/setup.cfg\n",
            "Running terminaltables-3.1.0/setup.py -q bdist_egg --dist-dir /tmp/easy_install-wodfcaun/terminaltables-3.1.0/egg-dist-tmp-bay7s2bc\n",
            "Moving terminaltables-3.1.0-py3.7.egg to /usr/local/lib/python3.7/dist-packages\n",
            "Adding terminaltables 3.1.0 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.7/dist-packages/terminaltables-3.1.0-py3.7.egg\n",
            "Searching for mmpycocotools\n",
            "Reading https://pypi.org/simple/mmpycocotools/\n",
            "Downloading https://files.pythonhosted.org/packages/99/51/1bc1d79f296347eeb2d1a2e0606885ab1e4682833bf275fd39c189952e26/mmpycocotools-12.0.3.tar.gz#sha256=b26f0b3504fad0be8fdb19f3cfa34d86f4ef97694bb403d172f9e7827781b539\n",
            "Best match: mmpycocotools 12.0.3\n",
            "Processing mmpycocotools-12.0.3.tar.gz\n",
            "Writing /tmp/easy_install-t5carztd/mmpycocotools-12.0.3/setup.cfg\n",
            "Running mmpycocotools-12.0.3/setup.py -q bdist_egg --dist-dir /tmp/easy_install-t5carztd/mmpycocotools-12.0.3/egg-dist-tmp-v7cvnpp5\n",
            "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /tmp/easy_install-t5carztd/mmpycocotools-12.0.3/pycocotools/_mask.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n",
            "       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n",
            "                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n",
            "       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n",
            "                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n",
            "   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n",
            "   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n",
            "                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n",
            "     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n",
            "                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[Kcommon/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "       if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n",
            "                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/lib/python3.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K/usr/local/lib/python3.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K/usr/local/lib/python3.7/dist-packages/numpy/core/include/numpy/arrayobject.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kpycocotools/_mask.c:612\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/lib/python3.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K-Wcpp\u001b[m\u001b[K]\n",
            " #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n",
            "  \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "zip_safe flag not set; analyzing archive contents...\n",
            "pycocotools.__pycache__._mask.cpython-37: module references __file__\n",
            "creating /usr/local/lib/python3.7/dist-packages/mmpycocotools-12.0.3-py3.7-linux-x86_64.egg\n",
            "Extracting mmpycocotools-12.0.3-py3.7-linux-x86_64.egg to /usr/local/lib/python3.7/dist-packages\n",
            "Adding mmpycocotools 12.0.3 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.7/dist-packages/mmpycocotools-12.0.3-py3.7-linux-x86_64.egg\n",
            "Searching for six==1.15.0\n",
            "Best match: six 1.15.0\n",
            "Adding six 1.15.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for numpy==1.19.5\n",
            "Best match: numpy 1.19.5\n",
            "Adding numpy 1.19.5 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.7 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for matplotlib==3.2.2\n",
            "Best match: matplotlib 3.2.2\n",
            "Adding matplotlib 3.2.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torchvision==0.9.1+cu101\n",
            "Best match: torchvision 0.9.1+cu101\n",
            "Adding torchvision 0.9.1+cu101 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torch==1.8.1+cu101\n",
            "Best match: torch 1.8.1+cu101\n",
            "Adding torch 1.8.1+cu101 to easy-install.pth file\n",
            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Cython==0.29.23\n",
            "Best match: Cython 0.29.23\n",
            "Adding Cython 0.29.23 to easy-install.pth file\n",
            "Installing cygdb script to /usr/local/bin\n",
            "Installing cython script to /usr/local/bin\n",
            "Installing cythonize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for setuptools==56.1.0\n",
            "Best match: setuptools 56.1.0\n",
            "Adding setuptools 56.1.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyparsing==2.4.7\n",
            "Best match: pyparsing 2.4.7\n",
            "Adding pyparsing 2.4.7 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for kiwisolver==1.3.1\n",
            "Best match: kiwisolver 1.3.1\n",
            "Adding kiwisolver 1.3.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for python-dateutil==2.8.1\n",
            "Best match: python-dateutil 2.8.1\n",
            "Adding python-dateutil 2.8.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for cycler==0.10.0\n",
            "Best match: cycler 0.10.0\n",
            "Adding cycler 0.10.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Pillow==7.1.2\n",
            "Best match: Pillow 7.1.2\n",
            "Adding Pillow 7.1.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for typing-extensions==3.7.4.3\n",
            "Best match: typing-extensions 3.7.4.3\n",
            "Adding typing-extensions 3.7.4.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Finished processing dependencies for mmdet==2.11.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cYEgifxIZai",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbef3467-df60-4c0d-fa3b-f43bc00c3c15"
      },
      "source": [
        "!git clone https://github.com/NVIDIA/apex\n",
        "%cd /content/Swin-Transformer-Object-Detection/apex\n",
        "!pip install  ./"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'apex'...\n",
            "remote: Enumerating objects: 8038, done.\u001b[K\n",
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            "remote: Compressing objects: 100% (91/91), done.\u001b[K\n",
            "remote: Total 8038 (delta 58), reused 68 (delta 29), pack-reused 7913\u001b[K\n",
            "Receiving objects: 100% (8038/8038), 14.11 MiB | 27.57 MiB/s, done.\n",
            "Resolving deltas: 100% (5457/5457), done.\n",
            "/content/Swin-Transformer-Object-Detection/apex\n",
            "Processing /content/Swin-Transformer-Object-Detection/apex\n",
            "Building wheels for collected packages: apex\n",
            "  Building wheel for apex (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for apex: filename=apex-0.1-cp37-none-any.whl size=204330 sha256=af3738f4e9c17667252a01d92561460d50e1510bf4031796436d3d2be8b05679\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-yjy0cfbx/wheels/d4/40/e2/0d874f21dba740f7d920044b0c8e2be3c64109f4eda6378a51\n",
            "Successfully built apex\n",
            "Installing collected packages: apex\n",
            "Successfully installed apex-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DA6ygEESAIio",
        "outputId": "d1aedaba-52bf-41a3-a7ea-c585aab3bdc9"
      },
      "source": [
        "%cd /content/Swin-Transformer-Object-Detection/"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Swin-Transformer-Object-Detection\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anZV88GxFkg0"
      },
      "source": [
        "!cp /content/drive/MyDrive/epoch_2.pth /content/Swin-Transformer-Object-Detection"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGAyXRJAF37k"
      },
      "source": [
        "!cp /content/drive/MyDrive/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py /content/Swin-Transformer-Object-Detection"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EqKpdwkInfl",
        "outputId": "b791165f-cb7c-414c-93f7-ed3482f08f09"
      },
      "source": [
        "!cat demo/image_demo.py\n",
        "!python demo/image_demo.py demo/demo.jpg configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py /content/Swin-Transformer-Object-Detection/mask_rcnn_swin_tiny_patch4_window7_1x.pth"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "from argparse import ArgumentParser\n",
            "\n",
            "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n",
            "\n",
            "\n",
            "def main():\n",
            "    parser = ArgumentParser()\n",
            "    parser.add_argument('img', help='Image file')\n",
            "    parser.add_argument('config', help='Config file')\n",
            "    parser.add_argument('checkpoint', help='Checkpoint file')\n",
            "    parser.add_argument(\n",
            "        '--device', default='cuda:0', help='Device used for inference')\n",
            "    parser.add_argument(\n",
            "        '--score-thr', type=float, default=0.3, help='bbox score threshold')\n",
            "    args = parser.parse_args()\n",
            "\n",
            "    # build the model from a config file and a checkpoint file\n",
            "    model = init_detector(args.config, args.checkpoint, device=args.device)\n",
            "    # test a single image\n",
            "    result = inference_detector(model, args.img)\n",
            "    # show the results\n",
            "    show_result_pyplot(model, args.img, result, score_thr=args.score_thr)\n",
            "\n",
            "\n",
            "if __name__ == '__main__':\n",
            "    main()\n",
            "Use load_from_local loader\n",
            "/content/Swin-Transformer-Object-Detection/mmdet/datasets/utils.py:68: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
            "  'data pipeline in your config file.', UserWarning)\n",
            "<Figure size 640.01x427.01 with 1 Axes>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hY8NLGTGC9O5"
      },
      "source": [
        "## 我也不知道为什么，但是就要重装这两个"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yI5pVY_BGq9",
        "outputId": "9799c237-6bee-408a-cd4b-c1857263fa65"
      },
      "source": [
        "!pip uninstall terminaltables -y\n",
        "!pip install terminaltables"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling terminaltables-3.1.0:\n",
            "  Successfully uninstalled terminaltables-3.1.0\n",
            "Collecting terminaltables\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/c4/4a21174f32f8a7e1104798c445dacdc1d4df86f2f26722767034e4de4bff/terminaltables-3.1.0.tar.gz\n",
            "Building wheels for collected packages: terminaltables\n",
            "  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp37-none-any.whl size=15356 sha256=4bcea34ca7f4fa809e2f31e8444dc5a68909b6dcb5c381daa2c28766001b78b5\n",
            "  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n",
            "Successfully built terminaltables\n",
            "Installing collected packages: terminaltables\n",
            "Successfully installed terminaltables-3.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3_ja5-9CvjT",
        "outputId": "c7e3089f-8641-40f1-f307-d37b3370baff"
      },
      "source": [
        "!pip uninstall timm -y\n",
        "!pip install timm"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling timm-0.4.5:\n",
            "  Successfully uninstalled timm-0.4.5\n",
            "Collecting timm\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/89/d94f59780b5dd973154bf506d8ce598f6bfe7cc44dd445d644d6d3be8c39/timm-0.4.5-py3-none-any.whl (287kB)\n",
            "\u001b[K     |████████████████████████████████| 296kB 26.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm) (0.9.1+cu101)\n",
            "Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from timm) (1.8.1+cu101)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision->timm) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->timm) (3.7.4.3)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.4.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "meghO-kaCLce",
        "outputId": "9a0e6d04-994e-4bab-e49e-b71744c07d7b"
      },
      "source": [
        "# Check Pytorch installation\n",
        "import torch, torchvision\n",
        "print(torch.__version__, torch.cuda.is_available())\n",
        "\n",
        "# Check MMDetection installation\n",
        "import mmdet\n",
        "print(mmdet.__version__)\n",
        "\n",
        "# Check mmcv installation\n",
        "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
        "print(get_compiling_cuda_version())\n",
        "print(get_compiler_version())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.8.1+cu101 True\n",
            "2.11.0\n",
            "10.1\n",
            "GCC 7.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZRMywugCVos"
      },
      "source": [
        "from terminaltables import AsciiTable"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYfXT9lu--zm",
        "outputId": "3df66f59-34e3-4384-a1bf-e8f5872a40a4"
      },
      "source": [
        "from argparse import ArgumentParser\n",
        "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n",
        "\n",
        "config_path = 'configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py'\n",
        "img_path = 'demo/demo.jpg'\n",
        "model_path = '/content/Swin-Transformer-Object-Detection/mask_rcnn_swin_tiny_patch4_window7_1x.pth'\n",
        "thres = 0.3\n",
        "# build the model from a config file and a checkpoint file\n",
        "model = init_detector(config_path, model_path, device='cuda:0')\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Use load_from_local loader\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZE3vqGoCFv1m",
        "outputId": "c6b659da-9834-417e-84f5-205ab3185726"
      },
      "source": [
        "from argparse import ArgumentParser\n",
        "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n",
        "\n",
        "config_path = '/content/Swin-Transformer-Object-Detection/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py'\n",
        "img_path = 'demo/0.png'\n",
        "model_path = '/content/Swin-Transformer-Object-Detection/epoch_2.pth'\n",
        "thres = 0.8 #准确度非常非常好，可以提高置信度\n",
        "# build the model from a config file and a checkpoint file\n",
        "model = init_detector(config_path, model_path, device='cuda:0')\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Use load_from_local loader\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "BInx4z0_GaPw",
        "outputId": "46659c3c-4130-4d00-c420-50bea1d63fe0"
      },
      "source": [
        "from IPython.display import Image \n",
        "pil_img = Image(filename=img_path)\n",
        "display(pil_img)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrvowTdFApPv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "a238f3ae-2046-4e40-c07e-8e403bb3bda0"
      },
      "source": [
        "# test a single image\n",
        "result = inference_detector(model, img_path)\n",
        "# show the results\n",
        "show_result_pyplot(model, img_path, result, score_thr=thres)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500.01x439.01 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "6iKnAcpyGwH6",
        "outputId": "c49606c4-7f42-46a4-941c-12c3f090e036"
      },
      "source": [
        "#并不属于测试集的情况\n",
        "img_path = 'demo/0001.jpg'\n",
        "# test a single image\n",
        "result = inference_detector(model, img_path)\n",
        "# show the results\n",
        "show_result_pyplot(model, img_path, result, score_thr=thres)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Swin-Transformer-Object-Detection/mmdet/datasets/utils.py:68: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
            "  'data pipeline in your config file.', UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 494.01x232.01 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "xptEwIRrHjgk",
        "outputId": "b155ffa8-7f02-44de-9447-fa7b97150356"
      },
      "source": [
        "#并不属于测试集的情况\n",
        "img_path = 'demo/74.png'\n",
        "# test a single image\n",
        "result = inference_detector(model, img_path)\n",
        "# show the results\n",
        "show_result_pyplot(model, img_path, result, score_thr=thres)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500.01x439.01 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}